Joint Optimization of Imperfect Preventive Maintenance and Production Scheduling for Single Machine Based on Game Theory Method

In this study,an optimization model of a single machine system integrating imperfect preventive maintenance planning and production scheduling based on game theory is proposed.The costs of the production department and the maintenance department are minimized,respectively.Two kinds of three-stage dynamic game models and a backward induction method are proposed to determine the preventive maintenance(PM)threshold.A lemma is presented to obtain the exact solution.A comprehensive numerical study is provided to illustrate the proposed maintenance model.The effectiveness is also validated by comparison with other two existed optimization models.

Imperfect preventive maintenance for numerical control machine tools with log-linear virtual age process

Based on the log-linear virtual age process, an imperfect preventive maintenance policy for numerical control(NC)machine tools with random maintenance quality is proposed. The proposed model is a combination of the Kijima type virtual age model and the failure intensity adjustment model. Maintenance intervals of the proposed hybrid model are derived when the failure intensity increase factor and the restoration factor are both random variables with uniform distribution. The optimal maintenance policy in infinite time horizon is presented. A numerical example is given when the failures of NC machine tools are described by the log-linear process. Finally, a discussion is presented to show how the optimal results depend on the different cost parameters.

A dynamic condition-based maintenance optimization model for mission-oriented system based on inverse Gaussian degradation process

An effective maintenance policy optimization model can reduce maintenance cost and system operation risk. For mission-oriented systems, the degradation process changes dynamically and is monotonous and irreversible. Meanwhile, the risk of early failure is high. Therefore, this paper proposes a dynamic condition-based maintenance(CBM) optimization model for mission-oriented system based on inverse Gaussian(IG) degradation process. Firstly, the IG process with random drift coefficient is used to describe the degradation process and the relevant probability distributions are obtained. Secondly, the dynamic preventive maintenance threshold(DPMT) function is used to control the early failure risk of the mission-oriented system, and the influence of imperfect preventive maintenance(PM)on the degradation amount and degradation rate is analysed comprehensively. Thirdly, according to the mission availability requirement, the probability formulas of different types of renewal policies are obtained, and the CBM optimization model is constructed. Finally, a numerical example is presented to verify the proposed model. The comparison with the fixed PM threshold model and the sensitivity analysis show the effectiveness and application value of the optimization model.

Reliability Modeling and Maintenance Policy Optimization for Deteriorating System Under Random Shock

Performance degradation and random shock are commonly regarded as two dependent competing risks for system failures. One method based on effective service age is proposed to jointly model the cumulative effect of random shock and system degradation, and the reliability model of degradation system under Nonhomogeneous Poisson processes(NHPP) shocks is derived. Under the assumption that preventive maintenance(PM) is imperfective and the corrective maintenance(CM) is minimal repair, one maintenance policy which combines PM and CM is presented. Moreover, the two decision variables, PM interval and the number of PMs before replacement,are determined by a multi-objective maintenance optimization method which simultaneously maximizes the system availability and minimizes the system long-run expect cost rate. Finally, the performance of the proposed maintenance optimization policy is demonstrated via a numerical example.